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Record W4388817550 · doi:10.1139/cjc-2023-0097

Interactional behaviour of analgesic drugs in aqueous solution of caffeine at different temperatures using multi-technique approach

2023· article· en· W4388817550 on OpenAlexvenueno aff
Aashima Beri, Rishi Kant, Tarlok S. Banipal

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicThermodynamic properties of mixtures
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryAqueous solutionAnalgesicIsentropic processMolarDrugIbuprofenCaffeineAbsorption (acoustics)Organic chemistryPharmacologyThermodynamics

Abstract

fetched live from OpenAlex

Drug combination therapies have been a promising strategy to overcome drug resistance. However, unexpected drug–drug interaction may cause adverse reactions, which puts patients in danger. Keeping this in mind, the interactions between pain killers, paracetamol (PCM), and ibuprofen sodium with caffeine (CAF) have been studied. Apparent molar volumes ( V 2,ϕ ) and apparent molar isentropic compression ( K s,2,ϕ ) for analgesic drugs in aqueous (1, 2, 5, and 10 mmol·kg −1 ) CAF solutions have been determined from measured densities, ρ, and speeds of sound, u, respectively, at T = 288.15–318.15 K and at pressure p = 101.3 kPa. The results have also been interpreted in terms of various interactions occurring in the mixed solution. Calorimetrically determined negative values of Δ tr Δ dil H 0 indicate the exothermicity and thus the occurrence of energetically more favorable process. The hyperchromic shift (UV absorption) in the spectra of PCM in aqueous solution of CAF suggests the dominance of solute–cosolute interactions through the hydrophobic–hydrophobic/hydrophilic groups ( 1 H NMR) of PCM + CAF system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.226
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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